(Senior) Machine Learning Engineer

Flagship Pioneering•Boston, MA

1d•$96,000 - $214,500

About The Position

ABOUT PROFOUND THERAPUETICS ProFound Therapeutics is pioneering the discovery of the expanded human proteome to unlock a new universe of potential therapeutics. By integrating multi-omics, advanced computation, and translational biology, we aim to reveal and characterize thousands of previously uncharted proteins and systematically explore their role in health and disease. THE ROLE We are seeking a highly motivated (Senior) Machine Learning Engineer / Data Scientist to join our AI/ML team. This individual will play a central role in designing and implementing advanced machine learning systems that integrate multi-omics, perturbation, and biological knowledge graph data. Working closely with the Head of AI/ML and cross-functional partners, you will develop generative, transformer-based, and causal models — including large language models (LLMs) — within a multi-agent causal AI framework to uncover disease-driving proteins and pathways. The insights generated will directly support therapeutic discovery and development.

Requirements

Ph.D. or M.S. in Computer Science, Physics, Computational Biology, Biostatistics, Applied Mathematics, or related field, with 3+ years of relevant post-graduate or industry experience.
Proven track record in machine learning model development, with expertise in transformers, graph neural networks, generative modeling, or causal inference.
Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, JAX, or PyTorch Geometric.
Strong background in probabilistic modeling, causal reasoning, or statistical inference.
Demonstrated ability to work in cross-disciplinary teams, communicate complex ideas clearly, and deliver results in fast-moving environments.

Nice To Haves

Experience working with multi-omics or high-dimensional biological data is strongly preferred.
Familiarity with knowledge graph technologies and graph databases is a plus.

Responsibilities

Architect and implement scalable ML systems that integrate multi-modal data (genomics, transcriptomics, proteomics, imaging, perturbation data).
Develop and deploy graph-based, transformer-based, and generative models (including LLMs) to capture biological relationships and simulate interventions.
Contribute to building a multi-agent causal AI framework that integrates causal graph learning, intervention simulation, and knowledge graph reasoning.
Collaborate with data engineering teams to design data pipelines that harmonize and prepare large-scale omics datasets for model training.
Implement, evaluate, and optimize causal inference methods (e.g., DAG learning, treatment-effect estimation, counterfactual modeling).
Partner with experimental scientists to ensure model outputs are biologically interpretable and experimentally testable.
Stay abreast of advances in ML/AI, causal modeling, and computational biology; bring innovative ideas into the team.